Accurate color representation has become an increasing issue although the techniques used to create accurate, real world image content has not changed drastically in over fifty years. In 1947, Loyd A. Jones and H. R. Condit submitted a paper to the Journal of the Optical Society of America. Their paper, published in 1948, described the techniques that the two used in attempting to create a better image representation of tones and brightness when developing pictures in their laboratory. As part of their process, the two took a picture of a scene with a standard camera. Then they took black and white measurement values for certain areas or portions of the scene. Some of the measurement values were taken with an exposure meter and included information regarding the location of the measured value. They wrote down their results and took everything back to their laboratory for processing.
During development of the picture, the two learned that they could change tone reproduction to get a better representation of the scene. Based upon their measured values, they could correlate the developing image to specific densities for corresponding areas or portions. If a measurement was made of the luminance of the green color of the grass or the luminance of the blue color of the sky, processing of the image resulted in a closer representation of what the scene in the picture actually looked like when they took the picture. However, information was still calculated and interpolated based upon those measurements taken by Mr. Jones and Mr. Condit as well as perceived guesses as to the correct values for missing variables, such as the luminance of a shadow not measured.
With the prolific development of computer technology, more accurate cameras, color measurement devices, and computers have led to more sophisticated and robust processing systems. With digital cameras, liquid crystal displays, and inkjet printers getting wider gamuts for recording, displaying, and/or outputting image content, the need for obtaining reference imagery to test the different algorithms utilized in each device has become increasingly greater. Today, one can calibrate a digital camera for image representation; however, one cannot calibrate an image as a reference for subsequent calibration of a camera and/or other device.
With the boom of Internet-related business increasing daily, companies are eager to ensure that products and information are being accurately represented. Clothing manufacturers distribute millions of catalogs a year. Year after year, hundreds of millions of dollars are spent on clothes by consumers who never actually see the end product in person until it arrives at their door. However, the number one reason for product return has consistently been the fact that the color shown in the picture, whether in a magazine, on a billboard, on the Internet, or in a catalog did not match the color of the end product when it was received. Problems of inaccurate image content can lead to millions of lost dollars for companies and consumers alike.
Today, the manual process of tone reproduction and image content creation occurs after all measured values have been taken. If a photographer fails to take enough measurements of different colors from a scene, he/she will either be forced to guess at certain variables when processing the image content of the picture or he/she will have to attempt to recreate the exact setting that the image was taken. Either scenario leaves highly inaccurate results as guessed variables leave accuracy to the memory of the user and environmental conditions, such as the temperature, wind pattern, lighting, and other variable, may have changed.
Internal limitations of the camera restrict the accurate representation of image content. Although one can calibrate the camera, the image taken by the camera is never properly calibrated to an accurate representation of the scene. Therefore, the calibrated camera of today may take pictures for processing that operates according to its calibration; however, if the camera may always bias certain or all variables in a certain manner because of the inaccurate calibration. For example, a camera may be calibrated with a less saturated blue color. Any subsequent highly saturated blue color will be lost by the calibration of the camera.